Aiming at the dependencies between items are difficult to be captured by the present session-based recommendation models from short sessions， with complex item interactions and dynamic user interest changes considered， a Session-based Recommendation of Enhanced Capsule Network （SR-ECN） model was proposed. First， session sequence data was processed by using the Graph Neural Network （GNN） to obtain embedded vector of each item. Then， the dynamic routing mechanism of the capsule network was used to aggregate high-level user preferences from the interaction history. In addition， a self-attention network was introduced by the proposed model to further consider potential information about users and items， thereby recommending more suitable items for users. Experimental results show that， on Yoochoose dataset， the proposed model is superior to all comparison models such as Session-based Recommendation with GNN （SR-GNN）， Target Attentive GNN （TAGNN）， and the proposed model improves 0.92 and 0.45 percentage points compared to the Lossless Edge-order preserving aggregation and Shortcut graph attention for Session-based Recommendation （LESSR） model in terms of recommendation recall and Mean Reciprocal Rank （MRR） respectively.